Multi-modality imaging systems perform diagnostic scans using multiple modalities, such as, for example, magnetic resonance (MR/MRI), computed tomography (CT), positron emission tomography (PET), and/or single photon emission computed tomography (SPECT). Multiple modalities are combined to provide complimentary and/or overlapping data sets. For example, MR scanning generally provides soft tissue morphological data and provides greater resolution of structural and functional characteristics of soft tissue, etc. PET scanning generally has a lower resolution but provides more useful information regarding the functional condition of the body tissues and systems such as the cardiovascular system. PET scanning is superior for indicating the presence of tumors or decreased blood flow to certain organs or areas of the body. The complementary strengths of two or more imaging modalities can be provided simultaneously by performing both methods in a single apparatus and imaging session.
During operation, image quality of one or more imaging modalities, such as a PET modality, can be affected by motion during imaging, for example, respiratory motion. When using a PET modality, imaging artifacts may be generated during image acquisition because of the respiratory motion. In multi-modality systems, the PET modality requires a relatively long duration data acquisition period, on the order of several minutes (e.g., about 2 to 30 minutes per image) for a typical clinically sufficient image. Typically, a large number of PET data acquisitions (e.g., frames) are acquired at many different time points during this period. Consequently, patient movement is a problem in PET scanning.
PET scanning has a limited field of view (FOV) and cannot capture whole body images. In order to perform whole body imaging, multiple PET images are captured at multiple positions with respect to a patient (e.g, beds). When stitching together multiple beds to form a single whole body PET image, motion effects and attenuation are most pronounced at the edges of the FOV (e.g., the edge voxels/slices). In multi-bed studies, breathing patterns of the patient can change between beds. Therefore, detection and compensation for the varying respiratory patterns is important for whole body PET reconstruction.
Single bed elastic motion correction algorithms are increasingly being used to model and compensate for respiratory motion in clinical PET images. If motion effects are not properly accounted for, image non-uniformity and incorrect quantification will occur. Although single bed elastic motion correction has been applied, motion correction for multi-bed PET data has remained challenging.